code: def precision_score(prediction, groundtruth):
      # PPV
      FP, FN, TP, TN = numeric_score(prediction, groundtruth)
      if (TP + FP) <= 0.0:
          return 0.0
  
      precision = np.divide(TP, TP + FP)
      return precision * 100.0
  
file_path: medicaltorch/medicaltorch/metrics.py
imports: from collections import defaultdict
  from scipy import spatial
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: precision_score(prediction, groundtruth) and returns: unknown, int